Low Complexity Image Compression of Capsule Endoscopy Images

Capsule endoscopy is a method for recording images of the digestive tract. A patient swallows a capsule containing a tiny camera, which captures images that are then transmitted wirelessly to an external receiver for examination by a physician. Due to limited computational capabilities in the capsule and bandwidth constraints derives from dimensions of capsule, low-complexity and efficient compression of the images is required before transmission. In addition, the images are captured using a Bayer filter mosaic, such that each pixel in raw captured images represents only one color: red, green or blue. This special format requires the adaptation of current compression schemes or development of new schemes.

In this project, we evaluate the performance of several existing compression methods and develop additional methods for compressing Bayer images. We begin with learning new run-length tables for JPEG compression of Bayer images, leading to an improvement of 2dB in PSNR compared to standard JPEG. We later transform the images into the YCgCo color space, which is more natural for representing endoscopic Bayer images. By applying several compression schemes in the YCgCo color space, additional improvement is obtained. In order to reduce computational complexity, we use the hardware-efficient Integer DCT transform, known as ICT, instead of the DCT transform used by JPEG.